#!/usr/bin/env python

# --------------------------------------------------------
# R-FCN
# Copyright (c) 2016 Yuwen Xiong
# Licensed under The MIT License [see LICENSE for details]
# Written by Yuwen Xiong
# --------------------------------------------------------

"""
Demo script showing detections in sample images.

See README.md for installation instructions before running.
"""

import _init_paths
from fast_rcnn.config import cfg
from fast_rcnn.test import im_detect
from fast_rcnn.nms_wrapper import nms
from utils.timer import Timer
# import matplotlib.pyplot as plt
import numpy as np
import scipy.io as sio
import caffe, os, sys, cv2
import argparse

CLASSES = ('__background__',
           'echinus', 'seastar', 'shell', 'trepang')

NETS = {'ResNet-101': ('ResNet-101',
                  'resnet101_rfcn_final.caffemodel'),
        'ResNet-50': ('ResNet-50',
                  # 'resnet50_rfcn_final.caffemodel')}
                  'resnet50_rfcn_ohem_iter_4000.caffemodel')}


def vis_detections(im, class_ind, dets, im_name, thresh=0.5):
    """Draw detected bounding boxes."""
    inds = np.where(dets[:, -1] >= thresh)[0]
    # print(dets[:, -1])
    if len(inds) == 0:
        # print(dets[:, -1])
        return

    # im = im[:, :, (2, 1, 0)]
    # fig, ax = plt.subplots(figsize=(12, 12))
    # ax.imshow(im, aspect='equal')
    for i in inds:
        bbox = dets[i, :4]
        score = dets[i, -1]
        cv2.rectangle(im, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (255-class_ind*40, 100+class_ind*20, class_ind*40), 2)
        cv2.putText(im, str(score), (bbox[0], bbox[1]), cv2.FONT_HERSHEY_SIMPLEX, 1, (255-class_ind*40, 100+class_ind*20, class_ind*40), 2)
    cv2.imshow("test", im)
    cv2.imwrite(im_name, im)
    # cv2.waitKey(1)
    #     ax.add_patch(
    #         plt.Rectangle((bbox[0], bbox[1]),
    #                       bbox[2] - bbox[0],
    #                       bbox[3] - bbox[1], fill=False,
    #                       edgecolor='red', linewidth=3.5)
    #         )
    #     ax.text(bbox[0], bbox[1] - 2,
    #             '{:s} {:.3f}'.format(class_name, score),
    #             bbox=dict(facecolor='blue', alpha=0.5),
    #             fontsize=14, color='white')

    # ax.set_title(('{} detections with '
    #               'p({} | box) >= {:.1f}').format(class_name, class_name,
    #                                               thresh),
    #               fontsize=14)
    # plt.axis('off')
    # plt.tight_layout()
    # plt.draw()

def demo(net, image_name):
    """Detect object classes in an image using pre-computed object proposals."""

    # Load the demo image
    im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)
    im = cv2.imread(im_file)
    # cv2.imshow("test", im)

    # Detect all object classes and regress object bounds
    timer = Timer()
    timer.tic()
    scores, boxes = im_detect(net, im)
    # print(boxes)
    timer.toc()
    print ('Detection took {:.3f}s for '
           '{:d} object proposals').format(timer.total_time, boxes.shape[0])

    # Visualize detections for each class
    CONF_THRESH = 0.8
    NMS_THRESH = 0.3
    for cls_ind, cls in enumerate(CLASSES[1:]):
        cls_ind += 1 # because we skipped background
        cls_boxes = boxes[:, 4:8]
        cls_scores = scores[:, cls_ind]
        dets = np.hstack((cls_boxes,
                          cls_scores[:, np.newaxis])).astype(np.float32)
        keep = nms(dets, NMS_THRESH)
        dets = dets[keep, :]
        vis_detections(im, cls_ind, dets, image_name, thresh=CONF_THRESH)

def parse_args():
    """Parse input arguments."""
    parser = argparse.ArgumentParser(description='Faster R-CNN demo')
    parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]',
                        default=0, type=int)
    parser.add_argument('--cpu', dest='cpu_mode',
                        help='Use CPU mode (overrides --gpu)',
                        action='store_true')
    parser.add_argument('--net', dest='demo_net', help='Network to use [ResNet-101]',
                        choices=NETS.keys(), default='ResNet-101')

    args = parser.parse_args()

    return args

if __name__ == '__main__':
    cfg.TEST.HAS_RPN = True  # Use RPN for proposals

    args = parse_args()

    prototxt = os.path.join(cfg.MODELS_DIR, NETS[args.demo_net][0],
                            'rfcn_end2end', 'test_agnostic.prototxt')
    caffemodel = os.path.join(cfg.DATA_DIR, 'rfcn_models',
                              NETS[args.demo_net][1])

    if not os.path.isfile(caffemodel):
        raise IOError(('{:s} not found.\n').format(caffemodel))

    if args.cpu_mode:
        caffe.set_mode_cpu()
    else:
        caffe.set_mode_gpu()
        caffe.set_device(args.gpu_id)
        cfg.GPU_ID = args.gpu_id
    net = caffe.Net(prototxt, caffemodel, caffe.TEST)

    print '\n\nLoaded network {:s}'.format(caffemodel)

    # Warmup on a dummy image
    im = 128 * np.ones((300, 500, 3), dtype=np.uint8)
    for i in xrange(2):
        _, _= im_detect(net, im)

    # im_names = ['echinus_0115.jpg', 'trepang_0062.jpg',
    #             'n02317335_17218.jpg', 'n01904806_8544.jpg']
    im_names = ['vs170622-025.jpg', 'vs170622-011.jpg', 'vs170622-049.jpg', 'vs170622-046.jpg']

    for im_name in im_names:
        print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
        print 'Demo for data/demo/{}'.format(im_name)
        demo(net, im_name)
        cv2.waitKey(0)
    # plt.show()
